Are there any advantages of using rules-based approaches versus models for detecting spam?
Yes, there would be a point. Assuming your rule set is accurate, then you can use data classified with it to train a model. This model can be expected to be more robust and properly categorise emails that your rule-set will not handle.

Why? Machine learning algorithms generally work on features, and identify relationships between those features that lead to a classification decision. A human rule author basically does the same, but they might not notice subtle relationships; a good ML algorithm, however, might pick those up.

So you could have a hybrid model, where you first use your rule-based classifier, and anything that does not get classified is then run through the ML classifier.